Document Type : Research Paper


1 Technical Institute of Babylon, Al-Furat Al-Awsat Technical University, Iraq Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.

2 Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.


Image classification depends substantially on the remote sensing method to generate maps of land use and land cover. This study used machine learning algorithms for classifying land cover, evaluating algorithms, and choosing the best way based on accuracy assessment matrices for land cover classifications. Satellite images from the Landsat by the United States Geological Survey (USGS) were used to classify the Babylon Governorate Land Use Land Cover (LULC). This study employed multispectral satellite images utilizing a spatial resolution of 30 meters and organized the data using three different algorithms to see the most accuracy. The process of categorization was carried out with the use of three distinct algorithms, which are as follows: Support Vector Machine (SVM), Mahalanobis Distance (MD), and Maximum Likelihood Classification (MLC). The classification algorithms utilized ArcGIS 10.8 and ENVI 5.3 software to detect four LULC classes: (Built-up Land, Water, Barren Land, and Agricultural Land). When applied to Landsat images, the results showed that the SVM approach gives greater overall accuracy and a larger kappa coefficient than the MD and MLC methods. SVM, MD, and MLC algorithms each have respective overall accuracy values of 86.88%, 85.00%, and 79.38%, respectively.

Graphical Abstract


  • SVM, MD, and MLC were used to classify LULC in Babylon Governorate.
  • SVM outperformed with 86.88% accuracy and a kappa of 0.83; MLC scored the lowest.
  • SVM (14.20, 0.99, 49.08, 35.73)%, MD (24.58, 1.07, 35.58, 38.78)%, and MLC (22.87, 0.93, 35.27, 40.93)% for each class.


Main Subjects

  1. Z. Jasim, Using of machines learning in extraction of urban roads from DEM of LIDAR data: Case study at Baghdad expressways, Iraq, Period. Eng. Nat. Sci., 7 (2019) 1710–1721.
  2. Jasim, K. Hasoon, and N. Sadiqe, Mapping LCLU Using Python Scripting, Eng. Technol. J., 37 (2019) 140–147.
  3. Hashem and P. Balakrishnan, Change analysis of land use/land cover and modelling urban growth in Greater Doha, Qatar, Ann. GIS, 21 (2015) 233–247.
  4. Rahman, S. Kumar, S. Fazal, and M. A. Siddiqui, Assessment of land use/land cover change in the North-West District of Delhi using remote sensing and GIS techniques, J. Indian Soc. Remote Sens., 40 (2012) 689–697.
  5. O. Omo-Irabor, A comparative study of image classification algorithms for landscape assessment of the Niger Delta Region, J. Geogr. Inf. Syst., 8 (2016) 163–170.
  6. Han, C. Yang, and J. Song, Scenario simulation and the prediction of land use and land cover change in Beijing, China, Sustainability, 7 (2015) 4260–4279.
  7. Ya’Acob, I. A. A. Jamil, N. F. A. Aziz, A. L. Yusof, M. Kassim, and N. F. Naim, Hotspots Forest Fire Susceptibility Mapping for Land Use or Land Cover using Remote Sensing and Geographical Information Systems (GIS), IOP Conf. Ser. Earth Environ. Sci., 1064 (2022).
  8. H. Al-Helaly, I. A. Alwan, and A. N. Al-Hameedawi, Land covers monitoring for Bahar-Al-Najaf (Iraq) based on sentinel-2 imagery, J. Phys. Conf. Ser., 1973 (2021) 012189.
  9. M. Kadhum, B. S. Jasim, and A. S. J. Al-saedi, Improving the spectral and spatial resolution of satellite image using geomatics techniques Improving the Spectral and Spatial Resolution of Satellite Image Using Geomatics Techniques, 2776 (2023) 040011.
  10. R. T. Ziboon, I. A. Alwan, and A. G. Khalaf, Utilization of remote sensing data and GIS applications for determination of the land cover change in Karbala Governorate, Eng. Technol. J., 31 (2013) 2773-2787.
  11. Głowienka and K. Michałowska, Analyzing the Impact of Simulated Multispectral Images on Water Classification Accuracy by Means of Spectral Characteristics, Geomatics Environ. Eng., 14 (2020) 47–58.
  12. E. Hussein, R. H. Hasan, and N. A. Aziz, Detecting the changes of AL-Hawizeh Marshland and surrounding areas using GIS and remote sensing techniques, Assoc. Arab Univ. J. Eng. Sci., 25 (2018) 53–63.
  13. A. Alwan, N. A. Aziz, and M. N. Hamoodi, Potential water harvesting sites identification using spatial multi-criteria evaluation in Maysan Province, Iraq, ISPRS Int. J. Geo-Information, 9 (2020) 235.
  14. Reis, Analyzing land use/land cover changes using remote sensing and GIS in Rize, North-East Turkey, Sensors, 8 (2008) 6188–6202.
  15. Dutta, A. Rahman, S. K. Paul, and A. Kundu, Changing pattern of urban landscape and its effect on land surface temperature in and around Delhi, Environ. Monit. Assess., 191 (2019) 1-15.
  16. Chen and J. Wang, Land use and land cover change detection using satellite remote sensing techniques in the mountainous Three Gorges Area, China, Int. J. Remote Sens., 31 (2010) 1519–1542.
  17. Pal and P. M. Mather, Assessment of the effectiveness of support vector machines for hyperspectral data, Futur. Gener. Comput. Syst., 20 (2004) 1215–1225.
  18. Thanh Hoan et al., Assessing the effects of land-use types in surface urban heat islands for developing comfortable living in Hanoi City, Remote Sens., 10 (2018) 1965.
  19. Rahman, S. P. Aggarwal, M. Netzband, and S. Fazal, Monitoring urban sprawl using remote sensing and GIS techniques of a fast growing urban centre, India, IEEE J. Sel. Top. Appl. earth Obs. Remote Sens., 4 (2010) 56–64.
  20. Kumari, M. Tayyab, H. T. Hang, M. F. Khan, and A. Rahman, Assessment of public open spaces (POS) and landscape quality based on per capita POS index in Delhi, India, SN Appl. Sci., 1 (2019) 1–13.
  21. E. Maxwell, T. A. Warner, and F. Fang, Implementation of machine-learning classification in remote sensing: An applied review, Int. J. Remote Sens., 39 (2018) 2784–2817.
  22. Adam, O. Mutanga, J. Odindi, and E. M. Abdel-Rahman, Land-use/cover classification in a heterogeneous coastal landscape using RapidEye imagery: evaluating the performance of random forest and support vector machines classifiers, Int. J. Remote Sens., 35 (2014) 3440–3458.
  23. Mokhtari and M. Akhoondzadeh, Data fusion and machine learning algorithms for drought forecasting using satellite data, J. Earth Sp. Phys., 46 (2020) 231–246.
  24. A. Alwan and N. A. Aziz, An accuracy analysis comparison of supervised classification methods for mapping land cover using sentinel 2 images in the al-hawizeh marsh area, southern iraq, Geomatics Environ. Eng., 15 (2021) 5–21.
  25. GÜNLÜ, Multispektral ve Birleştirilmiş Uydu Görüntüleri Kullanılarak Arazi Örtüsü Sınıflandırılmasında Farklı Sınıflandırma Yaklaşımlarının Karşılaştırılması: Ören Orman İşletme Şefliği Örneği, Bartın Orman Fakültesi Derg., 23 (2021) 1–1.
  26. W. Wang, B. M. Gebru, M. Lamchin, R. B. Kayastha, and W. K. Lee, Land use and land cover change detection and prediction in the kathmandu district of nepal using remote sensing and GIS, Sustain., 12 (2020) 3925.
  27. Ahmed, M. Muaz, M. Ali, M. Yasir, S. Ullah, and S. Khan, Mahalanobis distance and maximum likelihood based classification for identifying tobacco in Pakistan, in 2015 7th International Conference on Recent Advances in Space Technologies,RAST (2015) 255–260.
  28. O. Murtaza and S. A. Romshoo, Determining the suitability and accuracy of various statistical algorithms for satellite data classification, Int. J. geomatics Geosci., 4 (2014) 585–599.
  29. P. Zourarakis, Remote Sensing Handbook–Volume I: Remotely Sensed Data Characterization, Classification, and Accuracies, Photogramm. Eng. Remote Sens., 84 (2018) 481.
  30. Husein, O. Jasim, and S. Mahmood, Proposal of building a standard geodatabase for urban land use, MATEC Web Conf., 162 (2018) 1–5.
  31. Al-Anbari, Oday Zakariya, and Z. T. Mohammed, Environmental and Urban Land Use Analysis by GIS in AL-Shaab of Baghdad as a case study, Eng. Technol. J., 34 (2016) 2272-2281.
  32. M. Kadhum, B. S. Jasim, and M. K. Obaid, Change detection in city of Hilla during period of 2007-2015 using Remote Sensing Techniques, IOP Conf. Ser. Mater. Sci. Eng., 737 (2020) 012228.
  33. S. Jasim, Z. M. K. Al-Bayati, and M. K. Obaid, Accuracy of horizontal coordinates of cadastral maps after geographic regression and their modernization using gis techniques, Int. J. Civ. Eng. Technol., 9 (2018) 1395–1403.
  34. H. Al-Helaly, I. A. Alwan, and A. N. Al-Hameedawi, Assessing land cover for Bahar Al-Najaf using maximum likelihood (ML) and artificial neural network (ANN) algorithms, J. Phys. Conf. Ser., 1973 (2021) 012190.
  35. N. M. Al-Hameedawi, Comparison between Saaty’s approach and Alonso and Lamata’s approach in site selection process,” IOP Conf. Ser. Mater. Sci. Eng., 737 (2020) 012217.
  36. H. Hasan, A. N. M. Al-Hameedawi, and H. S. Ismael, Supervised Classification Model Using Google Earth Engine Development Environment for Wasit Governorate, IOP Conf. Ser. Earth Environ. Sci., 961 (2022) 012051.
  37. S. Ozigis, J. D. Kaduk, and C. H. Jarvis, Mapping terrestrial oil spill impact using machine learning random forest and Landsat 8 OLI imagery: A case site within the Niger Delta region of Nigeria, Environ. Sci. Pollut. Res., 26 (2019) 3621–3635.
  38. S. Ozigis, J. D. Kaduk, C. H. Jarvis, P. da Conceição Bispo, and H. Balzter, Detection of oil pollution impacts on vegetation using multifrequency SAR, multispectral images with fuzzy forest and random forest methods, Environ. Pollut., 256 (2020) 113360.
  39. Vapnik, The nature of statistical learning theory. Springer science & business media, 1999.
  40. Cherkassky, The nature of statistical learning theory~, IEEE Trans. Neural Networks, 8 (1997) 1564.
  41. K. Srivastava, D. Han, M. A. Rico-Ramirez, M. Bray, and T. Islam, Selection of classification techniques for land use/land cover change investigation, Adv. Sp. Res., 50 (2012) 1250–1265.
  42. Talukdar et al., Land-use land-cover classification by machine learning classifiers for satellite observations-A review, Remote Sens., 12 (2020) 1135.
  43. Mather and B. Tso, Classification methods for remotely sensed data. CRC press, 2016.
  44. Keshtkar, W. Voigt, and E. Alizadeh, Land-cover classification and analysis of change using machine-learning classifiers and multi-temporal remote sensing imagery, Arab. J. Geosci., 10 (2017) 1–15.
  45. G. Congalton and K. Green, Assessing the accuracy of remotely sensed data: principles and practices. CRC press, 2019.
  46. S. J. Al-Saedi, Z. M. Kadhum, and B. S. Jasim, Land Use and Land Cover Analysis Using Geomatics Techniques in Amara City, Ecol. Eng, 9 (2023) 161–169.
  47. Gworek and J. Rateńska, Mercury migration in pattern air-soil-plant, Ochr. Środowiska i Zasobów Nat., 41 (2009) 614-623.